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All analyses will use a backward selection approach to ascertain variables that have unique predictive associations with conversion at an initially liberal threshold of p ≤ 0.10.
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We chose independent variables that had a p-value ≤ 0.10 for inclusion in initial multivariate models and used a backward selection approach to select a final model for each outcome measure.
All covariates were selected using a backward selection (p<.05 to stay).
Based on 150 randomly selected 12.5 ha plots we identified mean age and basal area of oaks as the most important habitat factors using a backward selection logistic model.
A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population.
Starting with a full model including all 8 indices, the relative importance of indices was assessed using a backward selection procedure.
Finally, we looked for additional two-way interaction effects using a backward selection (p<.05 to stay) on the final model augmented with all two-way interaction effects.
Although the deviance of the final model suggested that main effects fit very well the data (deviance = 1,481 with DF = 2,106; p = 1.00), we looked for additional two-way interaction effects using a backward selection.
We used a backward selection procedure to identify significant confounders.
Prognostic factors were identified using a backward selection procedure.
We used a backward selection approach to develop a final predictive model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com